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Record W2738044110 · doi:10.1101/167411

ESCRT-Independent Surface Receptor And Transporter Protein Degradation By The ILF Pathway

2017· preprint· en· W2738044110 on OpenAlexafffund
Erin Kate McNally, Christopher L. Brett

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsESCRTEndosomeCell biologyEndocytosisBiologyTransport proteinLysosomeSaccharomyces cerevisiaeVesicular Transport ProteinsTransporterReceptorBiochemistryYeastIntracellularEnzyme

Abstract

fetched live from OpenAlex

SUMMARY Surface receptor and transporter protein down-regulation drives cell signaling, quality control and metabolism underlying diverse physiology. After endocytosis, proteins are delivered to endosomes where ESCRTs package them into intralumenal vesicles, which are degraded by acid hydrolases upon fusion with lysosomes. However, reports of ESCRT-independent surface protein degradation are emerging suggesting that alternative, non-canonical pathways exist. Using Saccharomyces cerevisiae as a model, here we show that in response to substrates, protein misfolding or TOR signaling, some internalized surface transporters (Hxt3, Itr1, Aqr1) bypass ESCRTs en route to the lysosome membrane where they are sorted into an area that is internalized as an intralumenal fragment (ILF) and degraded upon organelle fusion. This ILF pathway also degrades typical ESCRT client proteins (Mup1, Can1, Ste3) when ESCRT function is impaired. As the underlying machinery is conserved, we speculate that the ILF pathway is an important contributor to receptor and transporter down-regulation in all eukaryotes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.196
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2017
Admission routes2
Has abstractyes

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Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicCellular transport and secretionFrench-language works237,207